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Begin by logging into your ConvertKit account using your credentials. Ensure you have access rights to view and export data, which typically includes subscriber information, forms, and tags.
Once logged in, navigate to the Subscribers tab. Use the filter options to select the specific segment of data you wish to export (e.g., all subscribers, a particular tag, or a specific form). After selecting your desired data, look for the Export option, usually found in the top-right of the Subscribers tab. Click on Export, and ConvertKit will generate a CSV file, which will be sent to your registered email address.
Check your email for the CSV export link from ConvertKit. Click on the link provided to download the CSV file to your computer. Ensure you save it in a location that is easy to access, as you will need it for the next steps.
Go to Google Sheets (sheets.google.com) and log in with your Google account. Create a new, blank spreadsheet. This will be the destination for your ConvertKit data.
In your new Google Sheets document, click on "File" in the top menu, then select "Import." In the Import file dialog, choose the "Upload" tab, and then either drag your CSV file into the window or click "Select a file from your device" to browse and upload the CSV file downloaded from ConvertKit.
Once the file is uploaded, you'll be prompted with import settings. Choose to "Replace current sheet" or "Create new sheet" depending on how you want the data to appear. Set the separator type to "Comma" for CSV files. Review other settings like converting text to numbers if needed, then click “Import data” to finalize the process.
After the import is complete, review the data to ensure everything has been transferred correctly. Check for any formatting issues such as date formats, text alignment, or unintended truncation. Use Google Sheets' formatting tools to adjust columns, apply filters, or perform any additional data cleaning as necessary.
By following these steps, you can manually transfer data from ConvertKit to Google Sheets without relying on third-party tools.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
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ConvertKit's API provides access to a wide range of data related to email marketing campaigns. The following are the categories of data that can be accessed through ConvertKit's API:
1. Subscribers: This category includes data related to subscribers such as their email address, name, location, and subscription status.
2. Forms: This category includes data related to forms such as form ID, name, and the number of subscribers who have signed up through the form.
3. Tags: This category includes data related to tags such as tag ID, name, and the number of subscribers who have been tagged.
4. Sequences: This category includes data related to sequences such as sequence ID, name, and the number of subscribers who have been added to the sequence.
5. Broadcasts: This category includes data related to broadcasts such as broadcast ID, name, and the number of subscribers who have received the broadcast.
6. Automations: This category includes data related to automations such as automation ID, name, and the number of subscribers who have been added to the automation.
7. Metrics: This category includes data related to metrics such as open rates, click-through rates, and conversion rates for email campaigns.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
